Speaker Independent Continuous-Amharic Speech Recognizer: An Experiment Using Hybrid (HMM-ANN) System
| dc.contributor.advisor | Gamback, Bjorn (PhD) | |
| dc.contributor.advisor | Taddesse, Kinfe (PhD) | |
| dc.contributor.advisor | Seifu, Zegaye (PhD) | |
| dc.contributor.author | Seid, Hussien | |
| dc.date.accessioned | 2020-06-08T08:08:54Z | |
| dc.date.accessioned | 2023-11-18T12:45:22Z | |
| dc.date.available | 2020-06-08T08:08:54Z | |
| dc.date.available | 2023-11-18T12:45:22Z | |
| dc.date.issued | 2004-07 | |
| dc.description.abstract | Automatic speech recognition is a part of natural language processing which deals with making computers hear human speech and to take action according to what they heard. In line with this concept, Speaker Independent Continuous Amharic Speech Recognition is investigated to check the results that can be reached by an ANNIHMM hybrid approach. To implement the Amharic ASR system , 100 speakers, each speaking ten different sentences have been modeled with the help of the CSLU Toolkit . The model is constructed at a context dependent phoneme sub-word I eve!. A promising result of 78.56% word accuracy and 44.07% sentence recognition rate has been achieved for speaker dependant test and 74. 28% word accuracy and 43.70% sentence recognition rate for speaker independent test. These are the best figures reported so far for speech recognition for Amharic. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21464 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Speaker Independent Continuous-Amharic Speech Recognizer: An Experiment Using Hybrid (HMM-ANN) System | en_US |
| dc.type | Thesis | en_US |